1. Field of the Invention
The present invention relates generally to forming predictive performance models. More particularly, the present invention relates to estimating building performance.
2. Background Art
There is a desire to have aggressive performance improvement goals, such as energy reduction, for various buildings around the world. For example, developing accurate energy use estimates for a wide variety of existing and new buildings will enable facility managers to predict the optimum mix of capital improvements, occupant behavior incentives, and operational efficiency measures needed to meet these energy reduction goals.
Existing building simulation tools permit qualitative comparisons between design alternatives. These tools often fail to provide accurate quantitative predictions of real-world energy performance. Incomplete or inaccurate climate, occupancy, and behavioral data, as well as operational and maintenance constraints, are typical causes of this problem. Improved calibration can mitigate some errors for existing instrumented buildings, but it is often not feasible to install instruments, perform proper calibration, and verify results for the thousands of buildings found in large organizations.
FIG. 1 shows a diagram of a conventional approach to estimating the energy use of a building. As shown in FIG. 1, conventional process 100 utilizes a number of specific data inputs such as building geometry 102, weather conditions 104, internal energy loads, specifications for climate control systems 108 used by the building, details of operating strategies and schedules 110, and simulation specific parameters 112 that may vary for different versions of simulation engine 120 in use today. The described data inputs 102-112, or others like them, are fed into simulation engine 120, typically implemented as a black box system for producing results 130, which in the present example are intended to profile the energy use of the building under analysis.
Each of the data inputs 102-112, may themselves encompass numerous individual data values. For example, weather conditions 104 may include data from historical weather records, while internal energy loads 106 may include data describing the number of occupants per square foot of floor space, anticipated lighting needs, and the number of work stations within the building. Comparably extensive data sets may be required to fully characterize building geometry 102, and the number, size, type, and distribution of heating and air-conditioning units included in climate control systems 108.
Simulation engine 120 may utilize a set of sophisticated, but well understood thermodynamic equations to process data inputs 102-112 and provide energy use results 130. Unfortunately, it is well recognized in the art that despite the sophistication of the thermodynamics theory supporting the analysis performed by simulation engine 130, and despite the extensively detailed information typically provided by data inputs 102-112, conventional process 100 consistently provides inaccurate estimates of energy usage. In general, the errors resulting from the conventional approach to analysis just described are not due to flaws in simulation engine 120, or to insufficient comprehensiveness of data inputs 102-112. Rather, inaccuracies in results 130 flow primarily from inaccuracies in many or most of the specific data included in data inputs 102-112.
At first glance, it would seem that if all that is wrong with conventional process 100 is the accuracy of the data included in data inputs 102-112, the solution to the problem is as straightforward as improving the accuracy of that data. And it is true that a conventional approach to refining and improving results 130 may proceed by performing more precise measurements using more carefully calibrated instruments. That conventional solution can only go so far in improving the accuracy of results 130, however, for at least two important reasons.
One practical limitation to the accuracy that can be achieved by conventional process 100 or the like, is that the vast number of specific data being measured for entry into simulation engine 120 make it unlikely that all data can be provided with uniformly high accuracy. In light of the manner in which errors are known to propagate through calculations, significant errors in some measured data included in data inputs 102-112 can compromise the accuracy of results 130, regardless of the exquisite precision with which much of the other data may be recorded and entered into simulation engine 120. From a purely pragmatic standpoint, then, it appears that producing consistently reliable results from conventional process 100 may be practically impossible.
The problem of inaccuracy of results 130 is even more intractable than it seems from the foregoing discussion, however. For, even in the unlikely situation in which each data measurement performed for conventional process 100 is executed with perfect precision, and the thermodynamic equations used by simulation engine 120 are formulated with perfect rigor, results 130 are still not assured of accuracy. This is because many, if not most of the data used by simulation engine 120 cannot be precisely known, and can only be reasonably inferred. The reliability of simulation engine 120, however, is dependent upon the precision of its inputs. Thus, conventional process 100 consistently fails to produce accurate energy use estimates.
As a result of the described inaccuracy of conventional process 100, simulation engine 120 cannot be reasonably relied upon to produce quantitative results estimating energy use by a building. Instead, simulation engine 120 may be used to provide qualitative comparisons, for example, between different building geometries, or between buildings having the same building geometry located at different sites. While this outcome may have been an adequate, if less than ideal, compromise for planners in the past, the growing urgency of energy related considerations, such as the increasing cost of energy, the impact of energy consumption on global climate change, and the importance of energy independence to national security, have made the shortcomings of conventional approaches such as conventional approach 100 unacceptably limiting.
Accordingly, there is a need to overcome the drawbacks and deficiencies in the art by providing more efficient and accurate methods and systems for estimating building performance.